Passion at the heart of musicians’ well-being
Bibliographic record
Abstract
This article proposes that passion for music is an essential element in explaining the well-being of musicians. Based on the PERMA model of well-being and on research on passion for music, this article posits that being passionate about music, and more specifically holding a harmonious type of passion (HP), reduces music-related anxiety and enhances musicians’ life satisfaction, sense of psychological growth and mastery. Furthermore, it is expected that holding an obsessive passion (OP) toward music might thwart musicians’ well-being through increased musical anxiety. These hypotheses were tested with 225 trainee and expert classical musicians. In order to provide a valid measure of passion for music, the Passion Scale for Music (PSM) was first validated. Structural Equation Modelling (SEM) results provided support for the hypothesis that musicians who are passionate about music, and even more those who are HP, experience increased well-being, while OP does not contribute to musicians’ well-being. The relationships between passion and well-being in musicians were moderate to strong, confirming that the types of passion musicians hold is a central element in explaining their well-being. The article concludes that being passionate about music acts as a “sparkle” that brightens musicians’ lives with regards to their global well-being experience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".